論文

査読有り 国際誌
2013年7月31日

A counterfactual approach to bias and effect modification in terms of response types.

BMC medical research methodology
  • Etsuji Suzuki
  • ,
  • Toshiharu Mitsuhashi
  • ,
  • Toshihide Tsuda
  • ,
  • Eiji Yamamoto

13
開始ページ
101
終了ページ
101
記述言語
英語
掲載種別
研究論文(学術雑誌)
DOI
10.1186/1471-2288-13-101

BACKGROUND: The counterfactual approach provides a clear and coherent framework to think about a variety of important concepts related to causation. Meanwhile, directed acyclic graphs have been used as causal diagrams in epidemiologic research to visually summarize hypothetical relations among variables of interest, providing a clear understanding of underlying causal structures of bias and effect modification. In this study, the authors aim to further clarify the concepts of bias (confounding bias and selection bias) and effect modification in the counterfactual framework. METHODS: The authors show how theoretical data frequencies can be described by using unobservable response types both in observational studies and in randomized controlled trials. By using the descriptions of data frequencies, the authors show epidemiologic measures in terms of response types, demonstrating significant distinctions between association measures and effect measures. These descriptions also demonstrate sufficient conditions to estimate effect measures in observational studies. To illustrate the ideas, the authors show how directed acyclic graphs can be extended by integrating response types and observed variables. RESULTS: This study shows a hitherto unrecognized sufficient condition to estimate effect measures in observational studies by adjusting for confounding bias. The present findings would provide a further understanding of the assumption of conditional exchangeability, clarifying the link between the assumptions for making causal inferences in observational studies and the counterfactual approach. The extension of directed acyclic graphs using response types maintains the integrity of the original directed acyclic graphs, which allows one to understand the underlying causal structure discussed in this study. CONCLUSIONS: The present findings highlight that analytic adjustment for confounders in observational studies has consequences quite different from those of physical control in randomized controlled trials. In particular, the present findings would be of great use when demonstrating the inherent distinctions between observational studies and randomized controlled trials.

リンク情報
DOI
https://doi.org/10.1186/1471-2288-13-101
PubMed
https://www.ncbi.nlm.nih.gov/pubmed/23902658
PubMed Central
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3765813
ID情報
  • DOI : 10.1186/1471-2288-13-101
  • PubMed ID : 23902658
  • PubMed Central 記事ID : PMC3765813

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